Incorporate support vector machines to content-based image retrieval with relevant feedback

P. Hong, Q. Tian, T. S. Huang

Research output: Contribution to conferencePaperpeer-review

Abstract

By using relevance feedback [6], Content-Based Image Retrieval (CBIR) allows the user to retrieve images interactively. Begin with a coarse query, the user can select the most relevant images and provide a weight of preference for each relevant image to refine the query. The high level concept borne by the user and perception subjectivity of the user can be automatically captured by the system to some degree. This paper proposes an approach to utilize both positive and negative feedbacks for image retrieval. Support Vector Machines (SVM) is applied to classifying the positive and negative images. The SVM learning results are used to update the preference weights for the relevant images. This approach releases the user from manually providing preference weight for each positive example. Experimental results show that the proposed approach has improvement over the previous approach [5] that uses positive examples only.

Original languageEnglish (US)
Pages[d]750-753
StatePublished - Dec 1 2000
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: Sep 10 2000Sep 13 2000

Other

OtherInternational Conference on Image Processing (ICIP 2000)
CountryCanada
CityVancouver, BC
Period9/10/009/13/00

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Incorporate support vector machines to content-based image retrieval with relevant feedback'. Together they form a unique fingerprint.

Cite this